Let's face it; software systems have changed a lot over the last few decades. In this introductory talk we'll survey common architectures for software, AI, and data processing systems and discuss the concerns and tradeoffs that go into selecting a candidate systems architecture.

We'll cover traditional N-Tier application architectures as well as more recent innovations such event-driven and serverless architectures, microservices, modular monoliths and more. We'll also look at various data ingestion and processing pipelines and talk about the role of MLOps and Data Engineering in enterprise organizations. We'll close by tying everything together and seeing how AI systems can be deployed to orchestrate different data sources and external capabilities.

By the time we're done you'll have a solid understanding of the different ways of structuring modern software, AI, and data systems and when you might choose one approach over another.